HoloViews supports even high-dimensional datasets easily, and the standard mechanisms discussed already work well as long as you select a small enough subset of the data to display at any one time. However, some datasets are just inherently large, even for a single frame of data, and cannot safely be transferred for display in any standard web browser. Luckily, HoloViews makes it simple for you to use the separate Datashader library together with any of the plotting extension libraries, including Bokeh and Matplotlib. Datashader is designed to complement standard plotting libraries by providing faithful visualizations for very large datasets, focusing on revealing the overall distribution, not just individual data points.

Datashader uses computations accelerated using Numba , making it fast to work with datasets of millions or billions of datapoints stored in Dask dataframes. Dask dataframes provide an API that is functionally equivalent to Pandas, but allows working with data out of core and scaling out to many processors across compute clusters. Here we will use Dask to load a large Parquet-format file of taxi coordinates.

How does datashader work? ¶

• Tools like Bokeh map Data (left) directly into an HTML/JavaScript Plot (right)
• datashader instead renders Data into a plot-sized Aggregate array, from which an Image can be constructed then embedded into a Bokeh Plot
• Only the fixed-sized Image needs to be sent to the browser, allowing millions or billions of datapoints to be used
• Every step automatically adjusts to the data, but can be customized

When not to use datashader ¶

• Plotting less than 1e5 or 1e6 data points
• When every datapoint must be resolveable individually; standard Bokeh will render all of them
• For full interactivity (hover tools) with every datapoint

When to use datashader ¶

• Actual big data; when Bokeh/Matplotlib have trouble
• When the distribution matters more than individual points
• When you find yourself sampling, decimating, or binning to better understand the distribution

Load the data ¶

As a first step we will load a large dataset using Dask. If you have followed the setup instructions you will have downloaded a large Parquet-format file containing 12 million taxi trips. Let's load this data using dask to create a dataframe ddf :

Create a dataset ¶

In previous sections we have already seen how to declare a set of Points from a pandas dataframe. Here we do the same for a Dask dataframe passed in with the desired key dimensions:

We could now simply type points , and Bokeh would attempt to display this data as a standard Bokeh plot. Before doing that, however, remember that we have 12 million rows of data, and no current plotting program will handle that well in a web browser! Instead of letting Bokeh see this data, let's convert it to something far more tractable using the datashade operation. This operation will aggregate the data on a 2D grid, apply shading to assign pixel colors to each bin in this grid, and build an RGB Element (just a fixed-sized image) we can safely display in a browser:

If you zoom in you will note that the plot rerenders depending on the zoom level, which allows the full dataset to be explored interactively even though only an image of it is ever sent to the browser. The way this works is that datashade is a dynamic operation that also declares some linked streams. These linked streams are automatically instantiated and dynamically supply the plot_size , x_range , and y_range from the Bokeh plot to the operation based on your current viewport as you zoom or pan:

Adding a tile source ¶

Using the GeoViews (geographic) extension for HoloViews, we can display a map in the background. Just declare a Bokeh WMTSTileSource and pass it to the gv.WMTS Element, then we can overlay it:

Aggregating with a variable ¶

So far we have simply been counting taxi dropoffs, but our dataset is much richer than that. We have information about a number of variables including the base cost of a taxi ride, the fare_amount . Datashader provides a number of aggregator functions, which you can supply to the datashade operation. Here use the ds.mean aggregator to compute the average cost of a trip at a dropoff location:

Grouping by a variable ¶

Because datashading happens only just before visualization, you can use any of the techniques shown in previous sections to select, filter, or group your data before visualizing it, such as grouping it by the hour of day:

Additional features ¶

The actual points are never given directly to Bokeh, and so the normal Bokeh hover (and other) tools will not normally be useful with Datashader output. However, we can easily verlay an invisible QuadMesh to reveal information on hover, providing information about values in a local area while still only ever sending a fixed-size array to the browser to avoid issues with large data.

As you can see, datashader requires taking some extra steps into consideration, but it makes it practical to work with even quite large datasets on an ordinary laptop. On a 16GB machine, datasets 10X or 100X the one used here should be very practical, as illustrated at the datashader web site .

Onwards ¶

• The user guide explains in more detail how to work with large datasets using datashader.
• There is a sample bokeh app using this dataset and an additional linked stream that works well as a starting point.

The final sections of this tutorial will show how to bring all of the concepts covered so far into an interactive web app for working with large or small datasets.